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This research aims to develop a Medical Records Management (MRM) mobile application in hospitals. This application helps the healthcare employees for easy data retrieval and effective clinical decision making. Medical Records Management (MRM) for Android and iOS platforms, utilizing Low Code and No Code (LCNC) software development platforms was developed and implemented in hospital. This approach empowers healthcare professionals, including doctors, nurses, and Postgraduates (PGs), to efficiently access, update, and retrieve patient information, thereby improving communication and data sharing. The LCNC platform enables non-technical users to create a customized application without complex coding, streamlining the development process and accelerating time-to-market. This study uniquely assessed the risk using Failure Mode and Effect Analysis and identified the causes using Cause-and-Effect Analysis. By demonstrating the feasibility and benefits of LCNC in healthcare, this research contributes to enhancing medical record management, ultimately leading to improved patient outcomes and quality of care.
Article Highlights
Emphasis on the mobile app that is built with Zoho Creator helps healthcare providers easily store patient data, reducing errors and improving care.
The app was developed including modules for patient registration, medical records, and appointments, which enhances clinical decision-making.
Security measures like multi-factor authentication and regular updates was discussed which are crucial for data protection and app reliability.
Introduction
In recent years, there has been a growing need to digitize and streamline healthcare processes, including the management of medical records [1]. The advancement of mobile technology has revolutionized various sectors, including healthcare [2]. To address the inefficiencies and limitations of traditional paper-based medical record systems, a mobile health platform was developed to facilitate effective management of medical records in hospitals.
Paper-based medical record systems are prone to errors, delays, and difficulties accessing and sharing patient information across different departments [3]. These systems suffer from several challenges:
Manual documentation leads to transcription errors and incomplete records.
Paper storage takes up significant physical space and risks damage or loss.
Records retrieval is time-consuming, affecting workflow efficiency.
Lack of interoperability hinders coordinated care across departments.
To overcome these limitations, mobile health platforms for medical records leverage technologies like cloud computing, data encryption, and mobile applications. Key features include:
Integration with Electronic Medical Records (EMR) systems
Real-time record updates and access
Digital documentation and automated workflows
Secure sharing between healthcare professionals
These platforms aim to increase accessibility, efficiency, accuracy, and overall quality of care. Initial implementations have shown promising improvements across hospitals [4]. The flexibility of these systems allows adaptation to diverse healthcare settings and transformation of traditional medical records workflow.
Developing a mobile application concept draws inspiration from the integrated workflows of "Google Form and Google Drive." This mobile app constructed using Zoho Creators [36], a low-code and no-code platform, incorporates additional features. It facilitates the creation of applications with minimal coding, utilizing built-in components such as drag-and-drop, lookup, deluge script, run (workflow) script, refer field, and enable action, among others [13].
Mobile health platforms are increasingly being used for medical record management, though their effectiveness, implementation challenges, and impact require further evaluation. Recent research indicates mobile health platforms may enhance record management through improved recruitment, retention and usage, though traditional interventions remain an important comparison Fig. 1.
Fig. 1 [Images not available. See PDF.]
MRD basic prototype
MRD—mobile app
In this research, the prototype of a mobile app was developed that will keep tracking and maintaining the patient’s medical records from the registration process to discharge process in hospital. This mobile app will work with clouds storage platform, so that it can connect with all the employees involved in patients care like (Doctors, Nurses, Physicians, Nutritionist, Physiotherapist, Specialist, Administrative officers) in various shift timings. In this mobile app, there are 4 modules in this mobile app—User Identification ID, Patients Registration Form, Patients Medical Records, Appointment Booking for Patients before registration process this module can differentiate various categories access like environment access, admin access, MRD access and master access (control panel). This mobile app was developed using a zoho creator platform. Zoho Creator is a tool for building custom mobile apps for this research. Its low-code development approach makes it accessible to users with little or no coding experience, while its robust features and integrations make it a versatile tool for research studies of all kinds.
The basic concept of this mobile app is paper to paper less in medical records department in hospital and healthcare services.
The 4 modules are interconnected with each other, the run (workflow) script design from:
Module-1: User ID (Entry level data) and All User IDs (Overview Reports)
Module-2: Patients Registration Form (Entry level data) and Patient’s Registration Form Report (Overview Reports)
Module-3: Patients Medical Records (Entry level data) and Patients Medical Record Report (Overview Reports)
Module-4: Appointment Booking for Patients before registration process (Admin Access), it’s include New Appointment, Today Appointment, Appointment Request—(Pending, Confirm, Cancelled, Delete or Other)
These four modules are connecting features like “1st module to 2nd module” then “2nd module to 3rd module”, 4th module is separately accessed by admin.
Review of literature
Mobile health
In healthcare settings, mobile health platforms are being used more and more to enable remote access to patient data, which improves decision-making and cuts down on patient care delays [27]. Mobile health apps should prioritize protecting patient data; multi-factor authentication and encryption can safeguard private data and adhere to privacy regulations such as HIPAA [6].
Data synchronization
For smooth data sharing between various healthcare providers, mobile health platforms must be integrated with hospital electronic health records (EHRs) that are currently in place [32]. By enabling healthcare practitioners to swiftly and effectively explore medical records, user-centered design improves the usability of mobile health systems and improves patient care outcomes [35].Patients are empowered and their involvement in their care journey is enhanced when patient-centric elements like personalized health monitoring and real-time data access are designed [18]. Providers can obtain the most recent patient data thanks to real-time data synchronization between mobile devices and hospital systems, which minimizes information delays [29].
Wearable sensors
Wearable sensors and mobile health platforms can work together to provide ongoing health monitoring and enable prompt interventions [34]. Studies reveal that by simplifying procedures and decreasing paper-based paperwork, mobile health platforms lower hospital operating expenses [2]. Effective handoffs and continuity of care depend on healthcare teams being able to communicate in real time, which is made possible by mobile platforms [12]. Staff opposition and infrastructural constraints are frequent obstacles that call for appropriate training and a phased system rollout [23].
Data accuracy
Enhancing patient treatment plans and diagnosis accuracy is made possible by mobile technologies that provide access to comprehensive patient data [28]. Mobile health systems enhance patient safety by digitizing records, which lowers the possibility of errors related to manual entry [30]. Scalability should be included into mobile health platforms so they can expand to meet hospital demands and technological breakthroughs [17]. Privacy is a top priority, and studies indicate that platforms that adhere to healthcare rules and have strong data encryption work best [31]. Physicians can evaluate and consult on patient cases from any place by enabling remote access, which speeds up reaction times in urgent situations [5].
Patient data monitoring
Mobile health systems that use standardized formats provide uniform documentation, which facilitates data retrieval and inter-hospital cooperation [19]. Health records can be easily transferred between various healthcare settings thanks to mobile health platforms [1]. According to studies, mobile platforms enhance preventive care by offering continuous data tracking, which aids in the management of chronic diseases [14].
Telemedicine
Telemedicine services are made possible by mobile health systems, which enables hospitals to access more rural areas and minimize patient travel requirements [20]. Hospitals can detect trends and patterns in patient health by integrating data analytics, which makes pre-emptive and focused interventions possible [21]. Healthcare professionals can receive real-time warnings from mobile systems, guaranteeing prompt reactions to important patient occurrences [10]. Healthcare providers are able to concentrate more on providing direct patient care since automated mobile technologies lessen their administrative workload [9].Since system familiarity improves usability and lowers errors, effective training programs are essential for successful adoption [26].
Mobile health platforms
Mobile health platforms will continue to be shaped by emerging developments like artificial intelligence and machine learning, which will open up possibilities for personalized care [7]. The use of mobile health platforms significantly improves medical record accessibility, efficiency, and patient satisfaction. However, further research is needed to explore the long-term impact and cost-effectiveness of these platforms [16]. Patients and healthcare professionals generally perceive mobile platforms positively, citing benefits such as improved convenience, access to health information, and patient engagement [11] different implementation strategies of mobile health platforms for medical record management. Analysing 14 studies, the review identifies various approaches, including system-wide adoption, pilot projects, and phased implementation.
Security and privacy of patient data
The review highlights the importance of aligning implementation strategies with organizational needs, infrastructure, and stakeholder engagement for successful integration and improved outcomes. Martinez [24]. Mobile Record Management: A Review of Security and Privacy Considerations”. This review examines the security and privacy considerations associated with mobile health platforms in medical record management.
Stakeholder engagement
Analysing 12 studies, the review highlights the importance of robust security measures, such as encryption and authentication, to protect sensitive health information [33]. The significance of leadership support, organizational culture, and workflow integration in successful implementation. Clear communication, stakeholder engagement, and training programs are essential for overcoming resistance to change and ensuring effective utilization of these platforms [25].
Summary of literatures
The literature review identified several limitations, including data security, stakeholder engagement, training programs for healthcare professionals for implementing mobile health platforms, and continuous monitoring of patient data. This study aims to address these gaps by building on these identified challenges.
Methodology
Research design
The research design of this study is a descriptive type. Wherein the information’s was systematically obtained to describe the population, situation or phenomenon (Neuman 2014).The study employed a qualitative approach using a combination of primary and secondary data collection methods to gain in-depth insights into the current state of medical record management in a private multi-specialty hospital in Chennai. The study was conducted in a multispecialty hospital located in Chennai, Tamil Nadu.
Method of collecting data
Primary data
Direct observation of the medical records department workflow was conducted, focusing on aspects such as file storage, maintenance processes, file tracking, staff performance, and frequency of errors. Semi-structured interviews were held with staff working in the medical records department to gather their perspectives on the current system, including its strengths and weaknesses, challenges faced, and suggestions for improvement.
Secondary data
Existing documents and registers related to patient flow and standard procedures within the medical records department were reviewed to provide context and background information. Annual reports and other official documents published by the hospital were analyzed to understand its organizational structure, policies, and procedures related to medical record management.
This combined approach ensured a comprehensive understanding of the current state of medical record management in the hospital, enabling the identification of opportunities for improvement and the development of mobile app to enhance the quality healthcare delivery.
Therefore, the study aims to develop and evaluate a mobile application for medical records management and to assess system efficiency using user feedback by incorporating analytical methods like FMEA and cause-and-effect analysis.
It is important to be aware of the security and privacy risks associated with these platforms, as well as the implementation challenges that organizations may face.
Pilot study
Pilot study was conducted using Cronbach alpha test. Data reduction was done to identify the correct sample size.
Study design and sampling
To comprehensively capture diverse perspectives and ensure the inclusion of a range of viewpoints, we employed a purposive sampling strategy. The study targeted participants from various departments within the hospital, including mid-level and senior managers responsible for overseeing healthcare delivery across their respective departments. Potential participants were identified through a two- part process:
Departmental selection
We initially selected key departments whose functions are significantly intertwined with the medical records department, such as clinical departments (e.g., Emergency Medicine, Surgery), administrative departments (e.g., Medical Records, IT), and patient care departments (e.g., Nursing).
Managerial Selection
Within each selected department, we identified managers holding key decision-making roles due to their extensive knowledge and experience in managing healthcare processes and systems. Their direct or indirect involvement with the medical records department further strengthened their relevance to the study's focus.
The semi-structured interview process was conducted between May 2023 and June 2023, resulting in a sufficient sample that provided insightful and representative data for the study's objectives. The study was conducted for a period of 6 months in a private multi-specialty hospital.
Inclusion and exclusion criteria
The data was included from the medical record department and the data from other departments like radiology, general medicine was excluded in the study.
Results and findings
The mobile app was developed using 4 different modules in a Zoho platform. During the pilot phase, test users provided feedback, which the host used the developed app to progressively address and improve the app through a dedicated control panel. To effectively analyze and prioritize user concerns, tools like Urgent Matrix, FMEA (Failure Mode and Effects Analysis), and Cause-and-Effect diagrams were employed.
With further security enhancements and a revamped control panel featuring categorized channels, this mobile app demonstrates strong potential for implementation within the medical records department as an Electronic Medical Record (EMR) system.
Development of mobile app using low code and no code platform
Module-1 (User ID)
In Module 1 the mobile app's (Fig. 2) comprises various individuals who interact with patients within the hospital setting and utilize the app on their mobile devices. These include doctors, nurses, physicians, nutritionists, physiotherapists, administrative officers, medical record department staff, and even some additional referral doctors based on specific patient needs. To cater to these diverse user groups, the app is designed to address various functions throughout the patient journey. This includes facilitating patient registration and data entry, managing appointment scheduling and booking, streamlining the admission process and information access, enabling real-time patient information access and communication, supporting lab report integration and treatment information management, providing a central hub for Electronic Medical Records (EMR) access and management. By integrating these diverse functionalities, the mobile app aims to enhance communication, streamline workflows, and improve care delivery across all departments involved in patient care.
Fig. 2 [Images not available. See PDF.]
Module 1
Module-2 (Patient registration)
Patients can now conveniently register with our new mobile app. Simply provide basic information like name, date of birth, gender, and contact details to complete the registration process. This feature seamlessly connects with our admissions system, streamlining registration for both new and returning patients (Fig. 3).
Fig. 3 [Images not available. See PDF.]
Module 2
Module-3 (patient medical records): patients dashboard for medical information
The mobile app empowers hospital staff to efficiently manage the admission process. They can access and review patient registration details, schedule appointments seamlessly, and assign rooms with ease (Fig. 4). This feature seamlessly integrates with the patient registration process, ensuring accurate and up-to-date patient data throughout their admission journey.
Fig. 4 [Images not available. See PDF.]
Module 3
Patients can connect directly with healthcare providers via secure messaging to discuss their medical concerns, ask questions, and receive personalized advice. This feature (Fig. 4) integrates with medical records management, providing a centralized platform for patients to access information and communicate with their care team. Patients can conveniently access their prescriptions, request refills, and track their medication history directly through the app. This feature seamlessly connects with medical records, ensuring accurate and up-to-date medication information. Hospital staff can access, edit, and update patient health information directly within the app, streamlining the medical records management process. This feature integrates with patient registration and admissions, ensuring consistent and accurate patient- data throughout their care journey.
Module-4: appointment booking
The mobile app integrates a convenient appointment scheduling feature, allowing patients to book appointments with their healthcare providers directly (Fig. 5). This feature offers several benefits includes patients can schedule appointments at their own convenience, minimizing wait times in the clinic. This allows them to better manage their time and schedule and Improved Patient Satisfaction. The ability to book appointments online reduces stress and frustration for patients, leading to a more positive healthcare experience such as.
Fig. 5 [Images not available. See PDF.]
Module 4
Increased efficiency
By managing appointments online, healthcare providers can optimize their schedules and allocate resources more efficiently. This leads to improved patient flow and reduced wait times overall.
Enhanced accessibility
The mobile app provides 24/7 access to appointment scheduling, allowing patients to book appointments at any time, regardless of location. This is particularly beneficial for busy individuals or those living in remote areas.
Therefore, the developed mobile application incorporates evidence-based decision-making and discharge processes through real-time access to comprehensive patient data and automated workflow features, ensuring timely and informed clinical decisions. Additionally, the app addresses security and privacy concerns by implementing multi-factor authentication, end-to-end encryption, and regular security audits to safeguard sensitive health information and comply with industry standards.
Analysis
Urgent matrix and FMEA was used to evaluate the modules, to prioritize the task and to identify the failures at system level.
Urgent matrix
The Urgent Matrix, a common tool for task prioritization, categorizes tasks based on their Urgency and Importance levels. This matrix can be adapted from various prioritization frameworks used in healthcare, such as ABCDE and START triage. While the specific origin of the MoSCoW method (Must-Have, Should-Have, Could-Have, Won't-Have) is unknown, it's widely used in project management and software development for prioritizing requirements. Specific frameworks like DSDM or Agile methodologies often incorporate this technique. By combining the Urgent Matrix with the MoSCoW method, healthcare organizations can effectively prioritize tasks, allocate resources efficiently, and ensure timely delivery of critical services DSDM Consortium (2021) DSDM Atern Handbook, DSDM Consortium.
The application of the Urgent Matrix extends beyond individual tasks and can be effectively used to prioritize system-level processes. By categorizing processes based on their urgency and importance, organizations can optimize resource allocation, streamline workflows, and improve overall operational efficiency.
Applications in healthcare operations
Urgent and Important: Patient emergencies, critical medical procedures, and infectious disease outbreaks. Urgent but Not Important: Administrative tasks, routine patient check-ups, and non-urgent referrals. Important but Not Urgent: Staff training, quality improvement initiatives, and strategic planning. Neither Urgent nor Important: Non-critical administrative tasks, documentation updates, and non-urgent meetings.
By applying the Urgent Matrix to system-level processes, organizations can:
Prioritize critical tasks and avoiding unnecessary distractions, focus on high-impact activities and minimizing waste, Streamlining workflows and reducing bottlenecks, identify and mitigate potential risks and threats, and deliver timely and high-quality services.
Interpretation of urgent matrix
Table 1. Urgent matrix.
Urgent Matrix | Urgent | Not- Urgent |
|---|---|---|
Important | Quadrant 1 | Quadrant 2 |
Addressing data security breaches System crashes Critical user | Implementing new features to enhance user experience. Optimizing performance Improving data accuracy | |
Not- Important | Quadrant 3 | Quadrant 4 |
Could be resolving minor bugs Addressing user feedback Handling non-critical user support requests | Include cosmetic changes. Non-critical feature requests non-essential the documentation updates |
Source: Primary Data
Quadrant 1—urgent and important: This quadrant includes critical issues that require immediate attention (Table 1). Data security breaches pose a significant risk to patient privacy and should be addressed urgently to prevent unauthorized access and protect sensitive information and System crashes can disrupt the app’s functionality and impact user experience. Resolving system crashes promptly is crucial to ensure uninterrupted service and user satisfaction. User experience flaws that significantly impact usability and functionality should be addressed urgently to improve user satisfaction and prevent potential issues or frustrations.
Quadrant 2—important and not urgent: Items in this quadrant are important but don’t require immediate attention. They should be planned and addressed strategically. Enhancements to user experience can improve engagement and satisfaction. While important, these additions can be scheduled and prioritized based on development cycles or user feedback. Performance optimization focuses on improving the app’s speed, responsiveness, and efficiency. It is crucial for long-term success but can be planned and executed as part of optimization efforts or future releases and data accuracy is vital for reliable medical records management. While it’s important to maintain accurate data, improvements can be made over time through data validation processes and periodic updates.
Quadrant 3—not important and urgent: This quadrant includes items that are urgent but not necessarily important in the long run. They should be handled efficiently, but their long-term impact may be relatively lower. Minor bugs may not significantly impact the overall functionality, addressing them promptly can enhance the app’s stability and user experience. Responding to user feedback in a timely manner demonstrates attentiveness and improves user satisfaction. User feedback should be considered, but urgency may vary depending on the nature of the feedback and Non-critical user support requests, while urgent from the user’s perspective, may have a lower long-term impact. Efficiently handling these requests ensures satisfactory user support without diverting excessive resources.
Quadrant 4—not important and not urgent: Items in this quadrant have lower priority and can be addressed when resources and time permit. Cosmetic changes, such as visual enhancements or minor design adjustments, can improve aesthetics but may have minimal impact on the app’s functionality or user experience. Feature requests that are not essential or have a lower demand can be prioritized based on feasibility and long-term strategic planning and documentation updates that are not critical to the immediate use or understanding of the app can be scheduled for when there is available time and resources.
FMEA
Failure Modes and Effects Analysis (FMEA) as each row corresponds to a potential failure mode, its effects, and the associated severity, occurrence, detection ratings, and Risk Priority Number (RPN). Higher RPN values indicate higher risks.
Based on smart and mobile technologies that adapt to the human body, mobile technology can minimally invasive for health monitoring and significantly enhance patients' autonomy and quality of life. There is still minuscule knowledge regarding the system and application level failures in mobile health platform, to fill those gaps, Failure Mode and Effect Analysis (FMEA) was used in mobile health monitoring system. This analysis allowed to evaluate and identify the problems at system and application level in modern health monitoring system Marcello Cinque et al. [8]. Assessing possible danger during dialysis was made easier with the help of the healthcare failure mode and effect analysis system. Emergency resuscitation during haemodialysis was less common when the mobile application was used, and medical staff communication was greatly enhanced Lin et al. [22]. FMEA has been used extensively as a prospective reliability analysis technique to find and remove known and probable problems in designs, systems, goods, and services Hu-CheLiu et al. [15].
Interpretation of FMEA (failure mode and effects analysis)
The RPN (Risk Priority Number) is calculated by multiplying the severity, occurrence, and detection rankings together. The higher the RPN, the greater the risk. Based on the RPN scores given in the Table 2, the most critical failure modes include Incorrect password validation. This failure mode has the highest RPN score of 272. This means that it has the potential to cause a major impact if it occurs. It is also difficult to detect, which makes it even more risky and Confusing layout and navigation: This failure mode has an RPN score of 150. It is a high-risk failure mode that can occur occasionally. It is difficult to detect.
Table 2. FMEA (Failure Mode and Effects Analysis)
Source: Primary Data
There was an Incomplete data synchronization: This failure mode has an RPN score of 112. It is a medium-risk failure mode that can occur frequently. It is also moderately difficult to detect. This failure mode has an RPN score of 100. It is a high-risk failure mode that can occur rarely. It is also moderately difficult to detect. These failure modes should be addressed as a priority to reduce the overall risk of the system.
Some recommendations for addressing these failure modes: Implement strong password requirements, such as minimum length and complexity requirements at application level can reduce the errors. Use multi- factor authentication to add an extra layer of security. Improve the data synchronization process to make it more reliable. Implement retry mechanisms and error handling to recover from failures. Implement strong security controls to protect sensitive data. This includes encryption, access controls, and intrusion detection/prevention systems. Regularly back up data to ensure that it can be recovered in the event of a breach. By addressing these failure modes, we can significantly reduce the risk of system failure and improve the overall security and reliability of the system.
FMEA assigns severity ratings to potential failures, considering their impact on the system and its users. It also estimates the likelihood of each failure occurring, based on historical data, expert judgment, or statistical analysis. By evaluating system-level processes, FMEA can identify critical vulnerabilities that pose significant risks to the organization. By targeting system-level processes, FMEA can identify opportunities to enhance overall system performance and resilience. Billinton and Allan (1992). Reliability Evaluation of Engineering Systems: Concepts and Techniques. Plenum Press.
Interpretation for cause-and-effect analysis (fish bone)
From Fig. 6 the following causes and sub-causes have been identified.
Fig. 6 [Images not available. See PDF.]
Cause-and-Effect Analysis.
Source: Primary Data
Inaccurate patient data
Inaccurate patient data can negatively impact healthcare decisions, treatment outcomes, and patient safety and the causes are Insufficient data validation during entry this suggests that there might be inadequate checks or validation processes in place during the entry of patient data, leading to inaccuracies. The other cause is human errors can occur during the manual entry of patient data, resulting in inaccuracies, such as.
Lack of standardized data entry protocols
If there are no established protocols or guidelines for data entry, inconsistencies and errors can arise.
Data security breach
A data security breach can compromise patient privacy, lead to identity theft, and undermine trust in the healthcare system and the Sub Causes such as Outdated or weak encryption techniques can make the system vulnerable to unauthorized access and data breaches.Some of the encryption are Data Encryption Standard (DES): A symmetric-key algorithm that's no longer secure enough for modern use. Advanced Encryption Standard (AES): Uses a substitution permutation network to encrypt data. Rivest Shamir Adleman (RSA): An asymmetric encryption algorithm that uses a public key to encrypt data and a private key to decrypt it. Elliptic Curve Cryptography (ECC): A public key cryptography algorithm that's used for secure data transmission and digital signatures. Quantum cryptography: Uses quantum mechanical properties of particles to protect data. Inadequate management of user access rights and permissions can result in unauthorized users gaining access to sensitive data, Failure to regularly assess and update security measures can leave the system susceptible to emerging threats.
System downtime
System downtime can disrupt healthcare services, impact patient care, and cause frustration for users and the Sub Cause includes Insufficient server capacity or scalability i.e., if the system lacks the necessary resources or cannot handle increased user demand, it may experience downtime, Network connectivity issues such as Problems with network connections or infrastructure can disrupt the system’s availability, Inadequate backup and recovery mechanisms like insufficient backup and recovery processes can prolong system downtime in case of data loss or system failures, User interface usability issues can lead to user frustration, decreased productivity, and potential errors in medical records management, inconsistent user interface design and navigation patterns can confuse users and hinder their ability to perform tasks efficiently and unclear or ambiguous labelling of functions like poorly labelled functions or buttons can lead to user confusion and difficulty in understanding the system’s capabilities.
Comparison table
Author’s | Key insights | Challenges | Solutions and best practices |
|---|---|---|---|
Park and Jayaraman [27], Chen and Xu [6] | Mobile health platforms improve decision-making and reduce delays in patient care | Data security concerns, compliance with privacy regulations. | Multi-factor authentication, encryption, adherence to HIPAA. |
Smith and Brown [32], Zheng and Li [35], Khan and Roberts [18], Patel and Kim [29] | Integration with EHRs enhances seamless data sharing and usability | System compatibility issues, real-time access limitations. | User-centered design, real-time data synchronization, patient-centric features |
Williams and Thompson [34], Anderson and White [2], Green and Lee [12], Lopez and Chen [23] | Enables continuous health monitoring and timely interventions | Staff resistance, infrastructure limitations. | Proper training, phased implementation, real-time communication for continuity of care. |
Patel et al. [28], Ramirez and Ortiz [30], Jones and Garcia [17], Singh and Johnson [31], Baker and Lewis [5] | Improves treatment plans, diagnosis, and patient safety. | Risk of data errors, scalability concerns. | Digital records, scalable systems, strong encryption for privacy. |
Kim and Wong [19], Adams and Schmidt [1], Hernandez and Moore [14] | Facilitates standardized documentation, easy retrieval, and inter-hospital cooperation. | Compatibility with different healthcare settings. | Standardized formats, enhanced interoperability. |
Lee and Patel [20], Liu and Zhao [21], Garcia and Flores [10], Davis and Kim [9], Nguyen and Tran [26], | Expands healthcare access to rural areas, reduces patient travel, enhances real-time alerts. | Need for effective data analytics, usability concerns | Integration of AI for pattern detection, automated alerts, training programs. |
Chen and Yang [7], Johnson [16], Garcia [11] | Enhances accessibility, efficiency, and patient satisfaction. | Uncertainty about long-term impact and cost-effectiveness | Research on implementation strategies, system-wide adoption, phased implementation. |
Present study | Focus on the mobile health platform and data security | To introduce the Low Code/No Code (LCNC) platforms in healthcare | Matrix and FMEA analysis were carried out to understand the problems at system and application level in modern health monitoring system |
Discussion and conclusions
The implementation of the Medical Record Department (MRD) mobile app offers a promising solution to streamline medical record management and improve patient care within healthcare facilities. By providing a user- friendly interface and secure access through unique identification numbers, the app enables doctors, nurses, and Post Graduates to efficiently access and update patient records. This connectivity between healthcare professionals fosters collaboration and enhances the accuracy and completeness of medical reports. This study also helps for real time data synchronization for updating the data across multiple devices and databases.
The novelty of this study lies in its innovative application of Low Code/No Code (LCNC) platforms in healthcare settings, particularly in resource-limited environments. Unlike conventional app development methods that require extensive coding expertise and prolonged timelines, this approach empowers healthcare professionals with minimal technical knowledge to design and implement a functional mobile application tailored to their specific needs. By leveraging tools like Zoho Creator, the study not only accelerates the development process but also demonstrates how LCNC platforms can democratize technology adoption in the healthcare sector. This study focus on innovative development practices and robust analytical validation distinguishes the study as a pioneering effort in advancing digital health solutions that are both accessible and impactful in improving patient care and operational efficiency.
Implementing the MRD mobile app presents a compelling solution for streamlining medical record management and elevating patient care across healthcare institutions. The app's user-friendly interface and secure access facilitated by unique identification numbers empower doctors, nurses, and postgraduate trainees to efficiently access, update, and share patient records. This interconnectedness fosters collaboration amongst healthcare professionals, ultimately enhancing the accuracy and completeness of medical reports.
Various healthcare compliance framework can be used to protect the patient data. Healthcare compliance frameworks are regulatory and legal guidelines that ensure patient data security, ethical medical practices, and quality care delivery. These frameworks help healthcare organizations comply with data protection laws, patient safety regulations.
HIPAA (Health Insurance Portability and Accountability Act)—Protects patient data in the U.S.
GDPR (General Data Protection Regulation)—Regulates personal data protection in the EU.
HITECH (Health Information Technology for Economic and Clinical Health Act)—Strengthens HIPAA rules.
ISO 27799—Provides health informatics security best practices.
NHS DSP Toolkit (Data Security and Protection Toolkit)—Ensures compliance for healthcare organizations in the UK.
Implications for future research
Integration
Exploring the integration of medical technologies and systems within other departments with the mobile app for comprehensive healthcare data management.
Health bot
Introducing a dedicated Health Bot to the mobile app to further enhance patient- care. This Artificial Intelligence-powered tool has the potential to significantly impact healthcare delivery, offering readily accessible health information and assessments to the general public. With an estimated accuracy level of 80–90%, the health Bot empowers patients to understand their health status even before visiting the hospital, facilitating informed decisions and proactive treatment. Additionally, the development of the health Bot will complement and expand upon the existing features of the mobile app, creating a more holistic and patient-centric healthcare experience. The listed predictive analysis can be done to understand and predict the patient data.
Various ML models can be applied to predict mental health trends:
ML Model | Use case |
|---|---|
Logistic Regression | Predicts the likelihood of mental health issues based on risk factors |
Random Forest | Identifies important features contributing to mental health disorders |
Support Vector Machines (SVM) | Classifies individuals into high-risk or low-risk groups |
Recurrent Neural Networks (RNNs) / LSTMs | Analyzes time-series data from wearable devices for detecting mental health deterioration |
Natural Language Processing (NLP) | Detects mental health distress from social media text |
Autoencoders and Anomaly Detection | Identifies deviations from normal behavioral patterns |
Author contributions
SNP—Manuscript editing and correcting CY—Data collection and App development and first draft of the manuscript ABD—Data analysis and final editing of the manuscript All authors reviewed the manuscript.
Funding
The author(s) reported that there is no funding associated with the work featured in this article.
Data availability
The study contains both primary and secondary data from which analysis has been made and findings were concluded.
Declarations
Ethics approval and consent to participate
The study was approved by the Institute Ethical Committee (IEC), Sri Ramachandra Institute of Higher Education and Research SRIHER(DU) CSP/23/MAY/128/465.
Consent for publication
This study does not involve human participants, it is an observational study. Hence, the study does not require informed consent.
Competing interests
The authors declare no competing interests.
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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